{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Draw sample MNIST images from dataset\n",
    "Demonstrates how to sample and plot MNIST digits using Keras API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train labels:  {0: 5923, 1: 6742, 2: 5958, 3: 6131, 4: 5842, 5: 5421, 6: 5918, 7: 6265, 8: 5851, 9: 5949}\n",
      "Test labels:  {0: 980, 1: 1135, 2: 1032, 3: 1010, 4: 982, 5: 892, 6: 958, 7: 1028, 8: 974, 9: 1009}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x128a837f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import numpy as np\n",
    "from keras.datasets import mnist\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# load dataset\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "# count the number of unique train labels\n",
    "unique, counts = np.unique(y_train, return_counts=True)\n",
    "print(\"Train labels: \", dict(zip(unique, counts)))\n",
    "\n",
    "# count the number of unique test labels\n",
    "unique, counts = np.unique(y_test, return_counts=True)\n",
    "print(\"Test labels: \", dict(zip(unique, counts)))\n",
    "\n",
    "# sample 25 mnist digits from train dataset\n",
    "indexes = np.random.randint(0, x_train.shape[0], size=25)\n",
    "images = x_train[indexes]\n",
    "labels = y_train[indexes]\n",
    "\n",
    "# plot the 25 mnist digits\n",
    "plt.figure(figsize=(5,5))\n",
    "for i in range(len(indexes)):\n",
    "    plt.subplot(5, 5, i + 1)\n",
    "    image = images[i]\n",
    "    plt.imshow(image, cmap='gray')\n",
    "    plt.axis('off')\n",
    "\n",
    "# plt.savefig(\"mnist-samples.png\")\n",
    "plt.show()\n",
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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